sDLS - Distributed Least Squares localization for large Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspect of WSN research is location estimation. As a good solution of fine grained localization Reichenbach et al. introduced the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation which is performed on constrained sensor nodes to finalize the localization by adding locale knowledge. This allows to perform an originally complex calculation with high precision on constrained nodes. Besides this advantage, DLS lacks in two harmful constraints concerning practical appliance. On the one hand the algorithm does not scale, i.e. calculation and communication increases with the number of beacon nodes or with network size, respectively. On the other hand DLS even does not work for large networks. An important assumption of DLS is that each blind node can communicate with each beacon node to receive the precalculation and to determine distances to beacon nodes. In this work we present an adaptation of DLS, concerning major changes, which enables DLS to be used in large WSNs for the first time. At the same time computational and communicational cost of each node becomes independent from network size, while precision is kept on the same high level.

Improvements on scalable Distributed Least Squares localization for large Wireless Sensor Networks

“…The simulator provides a realistic radio communication model, including spatial and temporal normal distributed fading, random transmission errors, collisions and a CSMA-CA MAC layer. As previously done in [5], a static bidirectional spanning-tree routing was used for communication.…”

“…The described drawbacks have been overcome by scalable DLS (sDLS) [5], still saving the idea of DLS. The use of individual precalculations instead of only one precalculation for the whole network, as used by DLS, enabled sDLS to be used in large WSNs.…”

sDLSne - Improved scalable Distributed Least Squares localization with minimized communication

“…The described drawbacks have been overcome by scalable DLS (sDLS) [6], still saving the idea of DLS. Utilization of individual precalculations instead of only one precalculation, as done by DLS, enabled sDLS to be used in large WSNs.…”

Localization of static target in WSNs with least-squares and extended Kalman filter

“…Several methods are described in [18] through different measurements such as a distance and a direction, two directions, or three distances. Least-square (LS) was widely used for position estimation [19], [20]. A new method was presented by splitting the complex least-square algorithm into a less central precalculation and a simple, distributed subcalculation in [21].…”